Sunchay – a universal bookmarker that lets you peek inside your brain

Hacker News

I love taking notes and capturing ideas wherever I can: Apple Notes, Chrome bookmarks, Twitter bookmarks, etc. But I could never find out what I had saved unless I remembered where I saved it. I wanted a tool that could not only help me capture ideas, but rediscover them later. That's why I decided to build Sunchay. The goal is to eventually have a system which understands the kind of ideas that I like to save, and formulates a graph of inter-related ideas that can be surfaced later. Almost like a distillation of coherent thoughts. I have this thought that I should be able to talk to my brain and see the results with citations (article links, YT videos, personal notes, etc). I am also working on a Chrome extension that lets me quickly save anything that I am interested in. Would love to get your thoughts on this. Would you use something like this if it existed? I want to understand the kind of price that you would be willing to pay to use it? Any particular features that you would like to see here? Roast the design, question the stack, or riff on use-cases—from academic literature hoarders to chefs cataloguing recipe PDFs. Dry humor welcome. Reach out to me at hello [at] panchamkhaitan [dot] com. I also recorded a YouTube video to give a walkthrough: https://www.youtube.com/watch?v=DaxveexOHNY PS: I know that the UI is _very_ similar to some popular note-taking apps like MyMind or Fabric. I have taken cues from these tools. I will eventually make the personality more unique.

Visit product View on Hacker NewsMay 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, notes · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, video · Missing: mobile apps, ios, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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